Hyper-redundant sensor nodes

ABSTRACT

A hyper-redundant monitoring system and a gas turbine including the hyper-redundant monitoring system are provided. The hyper-redundant monitoring system includes a processor, a sensor node operably connected to the processor. The sensor node includes a plurality of sensors disposed in close proximity to one another such that a single parameter is measured by each of the plurality of sensors and each sensor is configured to transmit the parameter. The system also includes a power source and a controller in operable communication with the processor. The single parameter is output by each of the sensors and transmitted to the processor which collects the output parameters, analyzes the output parameters, and transmits analyzed data to the controller.

BACKGROUND 1. Field

The present disclosure relates generally to sensor measurement on a gasturbine engine and more particularly, to a hyper-redundant sensorconfiguration for monitoring parameters within a gas turbine engine.

2. Description of the Related Art

Sensor networks have been used for monitoring various parameters ofpower generation units within a power generation plant, for example, toavoid possible system failures. These sensor networks typically includewired sensors, which may be installed on the same power and signal linesas the power generation units. These wired networks may carry highinstallation costs due to the need for running additional power andsignal lines to each sensor, for example. Furthermore, existing sensornetworks employ a long measurement chain, in that, the sensor isconnected by lengthy cabling to data equipment which reads and analyzesthe incoming sensor data. Some effort has been made to introducewireless sensor networks within large industrial systems such as powergeneration units in a power generation plant, however, these effortshave met with security, power, and reliability issues.

As an example of a sensor used within a large industrial system,thermocouples are temperature sensors that are used within the turbinesection of a gas turbine engine to give an indication of the conditionof the rotor disc cavity. Each thermocouple sensor has wire leads comingout of the component that are connected back to a diagnostic unit.Instrumenting a plurality of thermocouples in this manner results in anextensive network of wires just for monitoring a single operatingcondition of temperature. With this extensive network of wires, comes anincreased probability of damage to the individual wires, which is thenumber one source of instrumentation failure.

Traditionally, existing sensor networks in a power generation plantinclude large, analog sensors which are somewhat reliable and accuratebut very expensive to operate. Transmitting analog data over the longmeasurement chain may also result in inaccuracies at the receiving end.Additionally, most sensors within a sensor network are not automaticallymonitored for drift, noise, location, and other important data. Thisdata, or metadata, can assist plant operators to know when a maintenancecondition is necessary requiring an outage, for example, or how to runthe power plant more efficiently.

New technology advances have developed low cost and low power computing,low cost sensing, and low cost digital data transmission. Most of thesedevices also have the added advantage of being small, such as in themillimeter (or smaller) range. For example, MEMs (Micro-electromechanical) sensors may range in size from 20 micrometeres to amillimeter. As an example of a new computing option, a Raspberry Picomputer may be as small as a postage stamp allowing it to be placed inthe proximity of the sensors. Due to the reduction in the cost of highperformance sensors, it is now feasible to arrange a plurality ofsensors in a hyper-redundant configuration.

SUMMARY

Briefly described, aspects of the present disclosure relates to ahyper-redundant monitoring system and a gas turbine engine including ahyper-redundant monitoring system.

A hyper-redundant monitoring system is provided. The hyper-redundantmonitoring system includes a processor, a sensor node operably connectedto the processor, a power source, and a controller in operablecommunication with the processor. The sensor node includes a pluralityof sensors disposed in close proximity to one another such that a singleparameter is measured by each of the sensors and each sensor isconfigured to transmit the parameter to the processor. The singleparameter is output by each of the sensors and transmitter to theprocessor. The processor then collects the output parameters by each ofthe sensors, analyzes the output parameters, and transmits analyzed datato the controller.

A gas turbine engine including the hyper-redundant monitoring system isalso provided. The analyzed data is used by the controller to determinea need for a maintenance condition of the gas turbine engine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a longitudinal view of a gas turbine engine includinga hyper-redundant monitoring system,

FIG. 2 illustrates a first embodiment of a hyper-redundant monitoringsystem, and

FIG. 3 illustrates a second embodiment of a hyper-redundant monitoringsystem.

DETAILED DESCRIPTION

To facilitate an understanding of embodiments, principles, and featuresof the present disclosure, they are explained hereinafter with referenceto implementation in illustrative embodiments. Embodiments of thepresent disclosure, however, are not limited to use in the describedsystems or methods.

The components and materials described hereinafter as making up thevarious embodiments are intended to be illustrative and not restrictive.Many suitable components and materials that would perform the same or asimilar function as the materials described herein are intended to beembraced within the scope of embodiments of the present disclosure.

In general, a redundant sensor configuration employs multiple sensors,each performing the same function, i.e., measuring the same parameter atthe same location. Likewise, a redundant computing configuration employsmultiple computers to perform the same tasks having one computerdesignated as ‘active’ and at least a further computer designated as the‘standby’ computer. Redundant configurations have been used to providesystem fault tolerance, which is the ability of a system to perform itstask after the occurrence of faults. For example, when a serious faultoccurs on the ‘active’ operating computer, a switch to anotherfunctional ‘standby’ computer may be made where it then becomes the‘active’ computer. Furthermore, a hyper-redundant configuration in whichthree or more redundant devices are employed may be used to moreaccurately and reliably provide measured data such as that data measuredby sensors in a gas turbine engine. As a result, a ‘soft’ failure modemay exist in which one or more sensors have failed without a totalfunctionality loss of the parameter measurement. Employing such aredundant monitoring system, may increase the reliability of such amonitoring system.

Referring now to the figures, where the showings are for purposes ofillustrating embodiments of the subject matter herein only and not forlimiting the same, FIG. 1 illustrates an embodiment of a longitudinalview of a gas turbine engine including a hyper-redundant sensormonitoring system. The hyper-redundant monitoring system may include acontroller which may be in operable communication with the gas turbineengine. Those skilled in the art would understand that the disclosedhyper-redundant monitoring system may be employed in many otherindustrial systems as well as the embodiment including a gas turbineengine as discussed, for exemplary purposes, below.

Referring to FIG. 1, an industrial gas turbine engine 10 is shown. Theengine 10 includes a compressor section 12, a combustor section 14, aturbine section 16, and an exhaust section or system 18. The combustorsection 14 includes a plurality of combustors 20. A hot working gas isconveyed from the combustor section 14 through to the turbine section16. A hyper-redundant monitoring system 30 is shown including a sensornode 32 comprising a plurality of sensors 36, the sensor node 32operably connected to a processor 34. In the shown embodiment, theprocessor 34 is a small, low cost computer. The sensors 36 are disposedin close proximity to one another such that each sensor 36 measures thesame parameter, for example, the same pressure. The sensor leads 38 areshown for illustrative purposes; these sensor leads 38 would be incontact with the parameter being measured within the gas turbine. Forexample, the sensor node 32 may be disposed within a rotor disc cavityof the gas turbine engine in order to measure a temperature of the rotordisc cavity. The hyper-redundant monitoring system 30 may also include acontroller 40 connected with the processor 34. The controller 40 may bein operable communication with the gas turbine 10 in order to use themeasured parameter data for controlling the gas turbine 10 or a powerplant. The parameter is output by each sensor 36, transmitted to the lowcost computer 34, where it is analyzed, the analyzed data thentransmitted to the controller 40. A power source 50 delivers power tothe sensor node 32 via the processor 34.

In the embodiment of FIG. 1, the sensor node 32 includes more than foursensors 36. Typically, each sensor node 32 in the hyper-redundantconfiguration comprises a number of sensors 36 in a range from 3 to 8sensors. Increasing the number of sensors 36 per node 32 has been shownto reduce sensor node failures exponentially. The sensors 36 may be of aMEMs structure or other monolithically produced sensor.

Each sensor 36 may measure the same parameter simultaneously or eachsensor 36 may measure the same parameter in time division. Measuring intime division may be defined, for purposes of the present disclosure, aspolling each sensor in sequence. For example, a sensor node 32comprising four sensors 36 may be polled by the processor 34 once asecond. The processor 34 would poll the sensors 36 in series, forexample, one at 0.2 sec, one at 0.4 sec., one at 0.6 sec. and the lastone at 0.8 sec, combine the readings, and produce an actual combinedmeasurement for once a second.

The processor 34 may be located in close proximity to the sensor node32. For example, the processor 34 may be located in a range of between 6inches and 20 feet from the sensor node 32. This is advantageous becausedigitization of the sensor signals may be accomplished very close to theorigination of data, minimizing the inaccuracy and cost associated withconserving the accuracy of analog signals. However, the processor 34 mayalso be located further away such as hundreds of feet from the sensornode 32. Having the processor 34 further away may be beneficialdepending on the environment in which the sensors are disposed and ifthe sensors are capable of digitizing their output data which wouldobviate the concerns associated with analog signal transmission.

Sensor types used in the hyper-redundant monitoring system 30 mayinclude thermocouples measuring temperature, pressure sensors measuringpressure, humidity sensors measuring the humidity at the location, levelsensors measuring gas or fluid levels, and actuator sensors that measurevalve or actuator positions, along with many other types of sensors. Oneskilled in the art would understand that other parameter measurementsmay also be possible.

A first embodiment of a hyper redundant monitoring system 30configuration is illustrated by FIG. 2 which includes a plurality ofhyper-redundant sensor nodes 32 connected to and controlled by a singleprocessor 34. In this embodiment, the processor 34 resides in the centerof a star-configuration of sensor nodes 32. Each sensor node 32 mayreceive power from a power source 50 via the processor 34 through awired connection. Sensor data may be transmitted via data lines to theprocessor 34. The sensor data may then be analyzed and compiled by theprocessor 34 and transmitted wirelessly to a wireless receiver 60 towhich a controller 40 has access.

A second embodiment of a hyper-redundant monitoring system configuration30 is illustrated by FIG. 3. In this embodiment, a plurality ofhyper-redundant sensor nodes 32 are connected to a processor 34 via awireless connection. The sensor nodes 32 may receive power through awired connection directly from the power source 50 to each sensor orpower may be received by energy harvesting at each sensor. In the shownembodiment, the sensor data is transmitted wirelessly to the processor34 to which the controller 40 has access. The processor 34 may reportthe analyzed data wirelessly, for example, using wireless LAN,Bluetooth, Wireless HART, or other protocols.

Similarly, to the redundant functionality the sensor node 32 provides,the functionality of the processor 34 may also be redundant with two ormore processors 34 communicating with one or more sensor nodes 32. Selfmonitoring parameters within the redundant processors may be used todetermine which processor 34 is the active one.

A single digital channel may carry all the parameter output data fromone or more sensor nodes 32 to the processor 34, reducing the number ofwires needed in the monitoring system 30. For example, a singleruggedized Cat 5/6 cable may be used to carry all the parameter outputdata.

Power may be delivered to each sensor node by various means. Forexample, as shown in the embodiment of FIG. 2, power may be deliveredvia a wire to each sensor node 32. In an alternate embodiment, eachsensor 36 may derive power by energy harvesting. As an example of energyharvesting, solar energy may be captured and stored for use by theprocessor 34 and delivered to each sensor 36 wirelessly. This embodimenteliminates the number of wires needed by not running a separate wire toeach sensor 36. Further, power may be delivered to the sensor node 32 bya data cable, for example a Cat5/6 data cable via a methodology calledPower Over Ethernet. Thus, in this embodiment the data and power arecarried by the same cable further reducing the number of wires needed.

The processor 34 may perform many functions including collecting theparameter outputs from the individual sensors, comparing the parameteroutputs, voting, analyzing the data, and reporting the data to thecontroller 40. The hyper-redundant functionality of the sensors 36enables the processor 34 the ability to compare the parameter data anddisregard the low and high readings for example. Algorithms running onthe processor 34 may analyze the output parameter to determine when asensor 36 may be faulty and predict when an individual sensor 36 mayrequire replacement.

As discussed previously, the redundancy enables the distinction of a‘soft’ failure mode for the sensor node 32 defined by one or moresensors being considered faulty and needing repair or replacement.However, because at least one sensor is functional and reportingreliable and accurate data, the sensor node 32 may continue functioningwith the ability to accurately measure the parameter for a longer periodof time. Trending of individual sensor failures may enable scheduling ofmaintenance during a scheduled outage before a total sensor node 32failure occurs. Additionally, the processor 34 may analyze the sensoroutput data providing statistics on the state and efficiency of thepower plant. For example, these statistics may produce standarddeviations, variances, relative drift, and other useful data.

Referring to FIGS. 1-3, a gas turbine engine 10 is also provided. Thegas turbine engine 10 includes a hyper-redundant monitoring system 30 asdescribed above. The analyzed data provided to the controller 40 by theprocessor 34 may be used to control aspects of the gas turbine engine 10including shutting down the gas turbine when a maintenance condition isneeded. For example, the processor 34 may predict when a sensor node 32will fail such that no individual sensors will be operable. Before thatoccurs, the processor 34 can predict when a maintenance condition may beneeded. With this information, the controller 40 may put the gas turbine10 into an outage condition so that the individual sensors 36 of thesensor node 32 may be replaced.

A processor 34 in close proximity to the sensor node 32 may digitize thedata and transmit the data in a different data formats precluding theneed for multiplexing equipment routinely used between the sensor node32 and the processor 34. Additionally, the data may be reconfigured toanother data format from a user remotely accessing the processor 34.

It may be appreciated that in operation, the disclosed hyper-redundantmonitoring system provides a very reliable, cost-effective solution tomeasuring various parameters on an industrial system. For example, thehyper-redundant sensor configuration enables a ‘soft’ failure modeallowing individual sensor failures without a total loss offunctionality. Additionally, the processor can detect and reportfailures to a system controller in order to conveniently schedule sensorreplacement and/or sensor repair. Integrating low cost computers and lowcost sensors significantly decreases costs associated with systemmonitoring. Adding wireless communication from the sensor node to theprocessor and/or from the processor to a wireless receiver and on to acontroller also eliminates costly wiring and failures due to wiringfaults. In one application, the processor analyzes the parameter outputdata to provide a statistical analysis on the data. This statisticaldata may be used to by a controller to run a gas turbine engine moreefficiently, for example.

While embodiments of the present disclosure have been disclosed inexemplary forms, it will be apparent to those skilled in the art thatmany modifications, additions, and deletions can be made therein withoutdeparting from the spirit and scope of the invention and itsequivalents, as set forth in the following claims.

What is claimed is:
 1. A hyper-redundant monitoring system, comprising:a processor; a sensor node operably connected to the processor andcomprising a plurality of sensors disposed in close proximity to oneanother such that a single parameter is measured by each of theplurality of sensors and each sensor is configured to transmitmeasurements of the single parameter to the processor; a power sourcethat delivers power to the processor; and a controller in operablecommunication with the processor, wherein the processor collects themeasurements of the single parameter by each of the plurality ofsensors, analyzes the measurements of the single parameter to determineanalyzed data, and transmits analyzed data to the controller, whereinthe controller uses the analyzed data to change operating parameters ona gas turbine, and wherein the processor is disposed in close proximityto the sensor node such that the processor is disposed within the gasturbine engine casing.
 2. The hyper-redundant monitoring system asclaimed in claim 1, wherein the plurality of sensors number in a rangeof three to eight sensors.
 3. The hyper-redundant monitoring system asclaimed in claim 1, wherein the single parameter is simultaneouslymeasured by each of the plurality of sensors.
 4. The hyper-redundantmonitoring system as claimed in claim 1, wherein the single parameter ismeasured in time-division by each of the plurality of sensors.
 5. Thehyper-redundant monitoring system as claimed in claim 1, wherein theprocessor is configured to determine when an output parameter by anindividual sensor in the sensor node is faulty, and wherein theprocessor ignores the faulty measurement of the single parameter by notincluding the faulty measurement of the single parameter in thetransmitted analyzed data.
 6. The hyper-redundant monitoring system asclaimed in claim 1, comprising a redundant computing network, theredundant computing network including the processor and a furtherprocessor, wherein the processor is configured to be redundant with thefurther processor, and wherein a self monitoring parameter within theredundant computing network is used to determine whether the processoror the further processor is active.
 7. The hyper-redundant monitoringsystem as claimed in claim 6, wherein a plurality of sensor nodes iscontrolled by the redundant computing network.
 8. The hyper-redundantmonitoring system as claimed in claim 1, wherein a plurality of sensornodes is controlled by the processor.
 9. The hyper-redundant monitoringsystem as claimed in claim 1, wherein a cable comprising a singledigital channel carries the measurements of the single parameter fromthe sensor node to the processor.
 10. The hyper-redundant monitoringsystem as claimed in claim 9, wherein the power source is delivered bythe cable to the sensor node.
 11. The hyper-redundant monitoring systemas claimed in claim 1, wherein the parameter is at least one oftemperature, pressure, humidity, fluid level, actuator position, andvibration.
 12. The hyper-redundant monitoring system as claimed in claim1, wherein the processor communicates with the controller by way ofwireless communication.
 13. The hyper-redundant monitoring system asclaimed in claim 1, wherein the sensor node communicates with theprocessor by way of wireless communication.
 14. The hyper-redundantmonitoring system as claimed by claim 13, wherein the plurality ofsensors harvest energy.
 15. The hyper-redundant monitoring system asclaimed by claim 1, wherein each of the plurality of sensors comprisinga MEMS structure.
 16. The hyper-redundant monitoring system as claimedin claim 1, wherein each of the plurality of sensors in the sensor nodeis monitored for drift, noise, and location.
 17. The hyper-redundantmonitoring system as claimed in claim 1, wherein the controller uses theanalyzed data to change operating parameters on a gas turbine.
 18. Thehyper-redundant monitoring system as claimed in claim 17, wherein theoperating parameters comprises one or more of pressure, temperature,humidity, vibration, actuator position, and fluid level.
 19. A gasturbine engine, comprising; a hyper-redundant monitoring system of claim1, wherein the analyzed data is used by the controller to determine aneed for a maintenance condition of the gas turbine engine, wherein whenthe control system determines a maintenance condition is needed thecontroller shuts down the gas turbine engine.